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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Genome structure and evolution of Antirrhnum majus L.

Snapdragon (Antirrhinum majus L.), a member of Plantaginaceae, is an important model for plant genetics and molecular studies on plant growth and development, transposon biology and self-incompatibility. Here we report a high-quality genome assembly of A. majus cultivated JI7 (A. majus cv.JI7) of a 510 Mb with 37,714 annotated protein-coding genes. The scaffolds covering 97.12% of the assembled genome were anchored on 8 chromosomes. Comparative and evolutionary analyses revealed that Plantaginaceae and Solanaceae diverged from their most recent ancestor around 62 million years ago (MYA). We also revealed the genetic architectures associated with complex traits such as flower asymmetry and self-incompatibility including a unique TCP duplication around 46-49 MYA and a near complete{psi} S-locus of ca.2 Mb. The genome sequence obtained in this study not only provides the first genome sequenced from Plantaginaceae but also bring the popular plant model system of Antirrhinum into a genomic age.

genomics↗

bioRxiv2018

The consensus molecular classification of muscle-invasive bladder cancer

Muscle-Invasive Bladder Cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, yielding diverse sets of subtypes, which hampers the clinical implications of such knowledge. Here, we report the results of a large international effort to reach a consensus on MIBC molecular subtypes. Using 1750 MIBC transcriptomes and a network-based analysis of six independent MIBC classification systems, we identified a consensus set of six molecular classes: Luminal Papillary (24%), Luminal Non-Specified (8%), Luminal Unstable (15%), Stroma-rich (15%), Basal/Squamous (35%), and Neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. This consensus system offers a robust framework that will enable testing and validating predictive biomarkers in future clinical trials.

cancer biology↗

bioRxiv2017

From word models to executable models of signaling networks using automated assembly

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community.\n\nStandfirst textINDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.\n\nHighlightsO_LIINDRA decouples the curation of knowledge as word models from model implementation\nC_LIO_LIINDRA is connected to multiple natural language processing systems and can draw on information from curated databases\nC_LIO_LIINDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formats\nC_LIO_LIWe used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language\nC_LI

systems biology↗

bioRxiv2018

The RhoGAP SPV-1 regulates calcium signaling to control the contractility of the C. elegans spermatheca during embryo transits.

Contractility of the non-muscle and smooth muscle cells that comprise biological tubing is regulated by the Rho-ROCK and calcium signaling pathways. Although many molecular details about these signaling pathways are known, less is known about how they are coordinated spatiotemporally in biological tubes. The spermatheca of the C. elegans reproductive system enables study of the signaling pathways regulating actomyosin contractility in live adult animals. The RhoGAP SPV-1 was previously identified as a negative regulator of RHO-1/Rho and spermathecal contractility. Here, we uncover a role for SPV-1 as a key regulator of calcium signaling. spv-1 mutants expressing the calcium indicator GCaMP in the spermatheca exhibit premature calcium release, elevated calcium levels, and disrupted spatial regulation of calcium signaling during spermathecal contraction. Although RHO-1 is required for spermathecal contractility, RHO-1 does not play a significant role in regulating calcium. In contrast, activation of CDC-42 recapitulates many aspects of spv-1 mutant calcium signaling. Depletion of cdc-42 by RNAi does not suppress the premature or elevated calcium signal seen in spv-1 mutants, suggesting other targets remain to be identified. Our results suggest SPV-1 works through both the Rho-ROCK and calcium signaling pathways to coordinate cellular contractility.\n\nHighlight SummaryThrough in vivo imaging of the calcium sensor GCaMP, we show that the RhoGAP SPV-1 is a key regulator of calcium signaling in the C. elegans spermatheca. Our data suggests SPV-1 acts at least partially through the small GTPase CDC-42 to modulate calcium signaling, while also acting on RHO-1 to modulate Rho-ROCK signaling. This places SPV-1 as a central regulator of cellular contractility.

cell biology↗

bioRxiv2017

Molecular characterization of breast and lung tumors by integration of multiple data types with sparse-factor analysis

Effective cancer treatment is crucially dependent on the identification of the biological processes that drive a tumor. However, multiple processes may be active simultaneously in a tumor. Clustering is inherently unsuitable to this task as it assigns a tumor to a single cluster. In addition, the wide availability of multiple data types per tumor provides the opportunity to profile the processes driving a tumor more comprehensively.\n\nHere we introduce Functional Sparse-Factor Analysis (funcSFA) to address these challenges. FuncSFA integrates multiple data types to define a lower dimensional space capturing the relevant variation. A tailor-made module associates biological processes with these factors. FuncSFA is inspired by iCluster, which we improve in several key aspects. First, we increase the convergence efficiency significantly, allowing the analysis of multiple molecular datasets that have not been pre-matched to contain only concordant features. Second, FuncSFA does not assign tumors to discrete clusters, but identifies the dominant driver processes active in each tumor. This is achieved by a regression of the factors on the RNA expression data followed by a functional enrichment analysis and manual curation step.\n\nWe apply FuncSFA to the TCGA breast and lung datasets. We identify EMT and Immune processes common to both cancer types. In the breast cancer dataset we recover the known intrinsic subtypes and identify additional processes. These include immune infiltration and EMT, and processes driven by copy number gains on the 8q chromosome arm. In lung cancer we recover the major types (adenocarcinoma and squamous cell carcinoma) and processes active in both of these types. These include EMT, two immune processes, and the activity of the NFE2L2 transcription factor.\n\nIn summary, FuncSFA is a robust method to perform discovery of key driver processes in a collection of tumors through unsupervised integration of multiple molecular data types and functional annotation.\n\nAuthor SummaryIn order to select effective cancer treatment, we need to determine which biological processes are active in a tumor. To this end, tumors have been quantified by high dimensional molecular measurements such as RNA sequencing and DNA copy number profiling. In order to support decision making, these measurements need to be condensed into interpretable summaries. Such summaries can be made interpretable by connecting them to biological processes.\n\nBiological process activity is continuous and multiple biological processes are taking place in a single tumor. Therefore, the biological processes associated with a tumor are misrepresented by clustering, which tries to put every tumor in a single cluster. In the method introduced in this paper (funcSFA), molecular measurements are summarized into a small number factors. A factor is a continuous value per tumor that aims to represent the activity of a biological process.\n\nWhen applied to breast and lung cancer, funcSFA identifies factors covering well known biology of these tumor types. FuncSFA also finds novel factors covering biology whose importance is not yet widely recognized in these tumor types. Some of the factors suggest treatment opportunities that can be further investigated in cell lines and mice.

bioinformatics↗

bioRxiv2018

A data mining paradigm for identifying key factors in biological processes using gene expression data

A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

bioinformatics↗

bioRxiv2016

NaviCom: A web application to create interactive molecular network portraits using multi-level omics data

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of molecular functions encompassed by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing to address specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signaling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease.

systems biology↗

bioRxiv2018

FUN-LOV: Fungal LOV domains for optogenetic control of gene expression and flocculation in yeast

Optogenetic switches permit accurate control of gene expression upon light stimulation. These synthetic switches have become a powerful tool for gene regulation, allowing modulation of customized phenotypes, overcoming the obstacles of chemical inducers and replacing their use by an inexpensive resource: light. In this work, we implemented FUN-LOV; an optogenetic switch based on the photon-regulated interaction of WC-1 and VVD, two LOV (Light Oxygen Voltage) blue-light photoreceptors from the fungus Neurospora crassa. When tested in yeast, FUN-LOV yields light-controlled gene expression with exquisite temporal resolution, and a broad dynamic range of over 1300-fold, as measured by a luciferase reporter. We also tested the FUN-LOV switch for heterologous protein expression in Saccharomyces cerevisiae, where Western blot analysis confirmed strong induction upon light stimulation, surpassing by 2.5 times the levels achieved with a classic GAL4/galactose chemical inducible system. Additionally, we utilized FUN-LOV to control the ability of yeast cells to flocculate. Light-controlled expression of the flocculin encoding gene FLO1, by the FUN-LOV switch, yielded Flocculation in Light (FIL), whereas the light-controlled expression of the co-repressor TUP1 provided Flocculation in Darkness (FID). Overall, the results revealed the potential of the FUN-LOV optogenetic switch to control two biotechnologically relevant phenotypes such as heterologous protein expression and flocculation, paving the road for the engineering of new yeast strains for industrial applications. Importantly, FUN-LOV s ability to accurately manipulate gene expression, with a high-temporal dynamic range, can be exploited in the analysis of diverse biological processes in various organisms.\n\nImportanceOptogenetic switches are molecular devices which allow the control of different cellular processes by light, such as gene expression, providing a versatile alternative to chemical inducers. Herein, we report a novel optogenetic switch (FUN-LOV) based on the LOV-domain interaction of two blue-light photoreceptors (WC-1 and VVD) from the fungus N. crassa. In yeast cells, FUN-LOV allowed tight regulation of gene expression, with low background in darkness and a highly dynamic and potent control by light. We used FUN-LOV to optogenetically manipulate, in yeast, two biotechnologically relevant phenotypes: heterologous protein expression and flocculation, resulting in strains with potential industrial applications. Importantly, FUN-LOV can be implemented in diverse biological platforms to orthogonally control a multitude of cellular processes.

microbiology↗

bioRxiv2018

A Single Adaptive Mutation in Sodium Taurocholate Cotransporting Polypeptide Induced by Hepadnaviruses Determines Virus Species-specificity

Hepatitis B virus (HBV) and its hepadnavirus relatives infect a wide range of vertebrates from fish to human. Hepadnaviruses and their hosts have a long history of acquiring adaptive mutations. However, there are no reports providing direct molecular evidence for such a coevolutionary \"arms race\" between hepadnaviruses and their hosts. Here, we present evidence suggesting the adaptive evolution of the sodium taurocholate cotransporting polypeptide (NTCP), an HBV receptor, has been influenced by virus infection. Evolutionary analysis of the NTCP-encoding genes from 20 mammals showed that most NTCP residues are highly conserved among species, exhibiting evolution under negative selection (dN/dS < 1); this observation implies that the evolution of NTCP is restricted by maintaining its original protein function. However, 0.7 % of NTCP amino acid (aa) residues exhibit rapid evolution under positive selection (dN/dS > 1). Notably, a substitution at aa 158, a positively selected residue, converting the human NTCP to a monkey-type sequence abrogated the capacity to support HBV infection; conversely, a substitution at this residue converting the monkey Ntcp to the human sequence was sufficient to confer HBV susceptibility. Together, these observations suggested that positive selection at aa 158 was induced by virus infection. Moreover, the aa 158 sequence determined attachment of the HBV envelope protein to host cell, demonstrating the mechanism whereby HBV infection would create positive selection at this residue in NTCP. In summary, we provide the first evidence in agreement with the function of hepadnavirus as a driver for inducing an adaptive mutation in host receptor.\n\nImportanceHepatitis B virus (HBV) and its hepadnavirus relatives infect a wide range of vertebrates, with a long infectious history (hundreds of millions of years). Such a long history generally allows adaptive mutations in hosts to escape from infection, while simultaneously allowing adaptive mutations in viruses to overcome host barriers. However, there is no published molecular evidence for such a coevolutionary \"arms race\" between hepadnaviruses and hosts. In the present study, we performed coevolutionary phylogenetic analysis between hepadnaviruses and the sodium taurocholate cotransporting polypeptide (NTCP), an HBV receptor, combined with virological experimental assays for investigating the biological significance of NTCP sequence variation. Our data provide the first molecular evidences supporting that HBV-related hepadnaviruses drive adaptive evolution in the NTCP sequence, including a mechanistic explanation of how NTCP mutations determine host viral susceptibility. Our novel insights enhance our understanding of how hepadnaviruses evolved with their hosts, permitting the acquisition of strong species-specificity.

evolutionary biology↗

bioRxiv2015

Olfactory receptors are sensitive to molecular volume of odorants

To study olfaction, first we should know which physical or chemical properties of odorant molecules determine the response of olfactory receptor neurons, and then we should study the effect of those properties on the combinatorial encoding in olfactory system.\n\nIn this work we show that the response of an olfactory receptor neuron in Drosophila depends on molecular volume of an odorant; The molecular volume determines the upper limits of the neural response, while the actual neural response may depend on other properties of the molecules. Each olfactory receptor prefers a particular volume, with some degree of flexibility. These two parameters predict the volume and flexibility of the binding-pocket of the olfactory receptors, which are the targets of structural biology studies.\n\nAt the end we argue that the molecular volume can affects the quality of perceived smell of an odorant via the combinatorial encoding, molecular volume may mask other underlying relations between properties of molecules and neural responses and we suggest a way to improve the selection of odorants in further experimental studies.

Neuroscience↗

bioRxiv2017

A systematic approach for dissecting the molecular mechanisms of transcriptional regulation in bacteria

Gene regulation is one of the most ubiquitous processes in biology. But while the catalog of bacterial genomes continues to expand rapidly, we remain ignorant about how almost all of the genes in these genomes are regulated. At present, characterizing the molecular mechanisms by which individual regulatory sequences operate requires focused efforts using low-throughput methods. Here we show how a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling can be used to dissect bacterial promoters in a systematic and scalable way. We demonstrate this method on both well-studied and previously uncharacterized promoters in the enteric bacterium Escherichia coli. In all cases we recover nucleotide-resolution models of promoter mechanism. For some promoters, including previously unannotated ones, the approach allowed us to further extract quantitative biophysical models describing input-output relationships. This method opens up the possibility of exhaustively dissecting the mechanisms of promoter function in E. coli and a wide range of other bacteria.

biophysics↗

bioRxiv2018

Systems Biology Theory Clarification of a Controversy in Pancreatic Beta Cell Regeneration

Whether new pancreatic beta-cells arise via pre-existing beta-cells or from differentiation of precursor cells - a question of fundamental importance for diabetic therapy - has long been debated. Recent experiments suggest that multipotent precursors from adult mouse pancreas, that give rise to beta-cells, do exist. However, such a finding is at odds with prior evidence that beta-cell expansions occurs exclusively through self-replication. Here we show that these two observations can be partially compatible. We use a systems biology approach to analyze the dynamics of the endogenous molecular-cellular network in the pancreas. Our results show that self-replicating beta-cells can themselves be multipotent precursors. In addition, our model predicts heterogeneity in beta-cell regeneration and suggests various differentiation paths of precursors. This work therefore provides a means of reconciling an apparent contradiction in the field, but also sheds light on possible paths of beta-cell regeneration from a systems biology perspective.

systems biology↗

bioRxiv2018

DRD2 and FOXP2 are implicated in the associations between computerized device use and psychiatric disorders

1.The societal health effects of ubiquitous computerized device use (CDU) is mostly unknown. Epidemiological evidence supports associations between CDU and psychiatric traits, but the underlying biological mechanisms are unclear. We investigated genetic overlaps, causal relationships, and molecular pathways shared between these traits using genome-wide data regarding CDU (UK Biobank; up to N=361,194 individuals) and Psychiatric Genomics Consortium phenotypes (14,477<N<150,064). The strongest genetic correlations were between \"weekly usage of mobile phone in last 3 months\" (PhoneUse) vs. attention deficit hyperactivity disorder (ADHD) (rg=0.425, p=4.59x10-11) and \"plays computer games\" (CompGaming) vs. schizophrenia (SCZ) (rg=-0.271, p=7.16x10-26). Latent causal variable analysis did not support causal relationships between these traits, but the observed genetic overlap was related to shared molecular pathways, including: dopamine transport (Gene Ontology:0015872, pSCZvsCompGaming=2.74x10-10) and DRD2 association (pSCZ=7.94x10-8; pCompGaming=3.98x10-25), and FOXP2 association (pADHD=9.32x10-7; pPhoneUse=9.00x10-11). Our results support epidemiological observations with genetic data, and uncover biological mechanisms underlying psychiatric disorders contribution to CDUs.

genomics↗

bioRxiv2017

Multi-generational silencing dynamics control cell aging

Cellular aging plays an important role in many diseases, such as cancers, metabolic syndromes and neurodegenerative disorders. There has been steady progress in identifying aging-related factors such as reactive oxygen species and genomic instability, yet an emerging challenge is to reconcile the contributions of these factors with the fact that genetically identical cells can age at significantly different rates. Such complexity requires single-cell analyses designed to unravel the interplay of aging dynamics and cell-to-cell variability. Here we use novel microfluidic technologies to track the replicative aging of single yeast cells and reveal that the temporal patterns of heterochromatin silencing loss regulate cellular lifespan. We found that cells show sporadic waves of silencing loss in the heterochromatic ribosomal DNA (rDNA) during the early phases of aging, followed by sustained loss of silencing preceding cell death. Isogenic cells have different lengths of the early intermittent silencing phase that largely determine their final lifespans. Combining computational modeling and experimental approaches, we found that the intermittent silencing dynamics is important for longevity and is dependent on the conserved Sir2 deacetylase, whereas either sustained silencing or sustained loss of silencing shortens lifespan. These findings reveal, for the first time, that the temporal patterns of a key molecular process can directly influence cellular aging and thus could provide guidance for the design of temporally controlled strategies to extend lifespan.\n\nSignificanceAging is an inevitable consequence of living, and with it comes increased morbidity and mortality. Novel approaches to mitigating age-related chronic diseases demand a better understanding of the biology of aging. Studies in model organisms have identified many conserved molecular factors that influence aging. The emerging challenge is to understand how these factors interact and change dynamically to drive aging. Using multidisciplinary technologies, we have revealed a sirtuin-dependent intermittent pattern of chromatin silencing during yeast aging that is crucial for longevity. Our findings highlight the important role of silencing dynamics in aging, which deserves careful consideration when designing schemes to delay or reverse aging by modulating sirtuins and silencing.

systems biology↗

bioRxiv2017

The role of traction in membrane curvature generation

Curvature of biological membranes can be generated by a variety of molecular mechanisms including protein scaffolding, compositional heterogeneity, and cytoskeletal forces. These mechanisms have the net effect of generating tractions (force per unit length) on the bilayer that are translated into distinct shapes of the membrane. Here, we demonstrate how the local shape of the membrane can be used to infer the traction acting locally on the membrane. We show that buds and tubes, two common membrane deformations studied in trafficking processes, have different traction distributions along the membrane and that these tractions are specific to the molecular mechanism used to generate these shapes. Furthermore, we show that the magnitude of an axial force applied to the membrane as well as that of an effective line tension can be calculated from these tractions. Finally, we consider the sensitivity of these quantities with respect to uncertainties in material properties and follow with a discussion on sources of uncertainty in membrane shape.

biophysics↗

bioRxiv2018

Learning of pathogenic bacteria in adult C. elegans bidirectionally regulates pathogen response in the progeny

Parental experience can generate adaptive changes in the behavioral and physiological traits of the offspring1-3. However, the biological properties of this intergenerational regulation and the underlying molecular and cellular mechanism are not well understood. Here, we show that the experience of learning to avoid pathogenic bacteria in C. elegans alters the behavioral response to the pathogen in the progeny through the endogenous RNA interference (RNAi) pathway. We previously show that the adult C. elegans learns to avoid the smell of pathogenic bacteria, such as the Pseudomonas aeruginosa strain PA14, after feeding on the pathogen for a few hours4,5. Here, we report that this learning experience can bidirectionally regulate the olfactory response to PA14 in the progeny that are never directly exposed to the pathogen. The olfactory preference for PA14 in these progeny is linearly correlated with the learned avoidance of PA14 in their mothers. If the mothers show strong learning of PA14, their progeny avoid PA14; intriguingly, if the mothers show weak learning of PA14, the progeny prefer PA14, suggesting that the PA14-trained mothers transmit both the negative and positive information of PA14 to their progeny. The intergenerational behavioral effect results from an altered behavioral decision regulated by an olfactory sensorimotor neural circuit. Learning to avoid the pathogen also influences the development of the progeny, which is regulated independently from the behavioral change. Animals mutated for the RRF-3/RNA-directed RNA polymerase, a master regulator for the synthesis of the small interfering RNAs that are maternally inherited or in the soma6,7, display the normal naive and learned response to PA14 but are defective in regulating the olfactory response to PA14 in their progeny. Our results characterize an intergenerational effect that allows the progeny to rapidly adapt to an environmental condition that is critical for survival.

neuroscience↗

bioRxiv2017

Airway IRF7hi versus IRF7lo molecular response patterns determine clinical phenotypes in children with acute wheezing

Asthma exacerbations are triggered by rhinovirus infections. We employed a systems biology approach to delineate upper airway gene network patterns underlying asthma exacerbation phenotypes in children. Cluster analysis unveiled distinct IRF7hi versus IRF7lo molecular phenotypes, the former exhibiting robust upregulation of Th1/type I interferon responses and the latter an alternative signature marked by upregulation of cytokine and growth factor signalling and downregulation of interferon gamma. The two phenotypes also produced distinct clinical phenotypes. For IRF7lo versus IRF7hi: symptom duration prior to hospital presentation was more than twice as long from initial symptoms (p=0.011) and nearly three times as long for cough (p<0.001); the odds ratio of admission to hospital was increased more than four-fold (p=0.018); and time to recurrence was shorter (p=0.015). In summary, our findings demonstrate that asthma exacerbations in children can be divided into IRF7hi versus IRF7lo phenotypes with associated differences in clinical phenotypes.\n\nAbbreviationsAHR, airway hyperresponsiveness; ARG1, Arginase 1, CSF3, Colony Stimulating Factor 3; CD38, Cluster of Differentiation 38; CD163, Cluster of Differentiation 163; cDCs, conventional (or myeloid) dendritic cells; DDX60, DExD/H-Box Helicase 60; ED, Emergency Department; EGF, Epidermal Growth Factor; ERK, Extracellular signal-Regulated Kinase; FCER1G, Fc Fragment Of IgE Receptor Ig; HMBS, Hydroxymethylbilane Synthase; IFNg, Interferon Gamma; IFNL1, Interferon Lambda 1; IL-1R2, Interleukin 1 Receptor Type 2; IRF7, Interferon Regulatory Factor 7; ISG15, Interferon-stimulated gene 15; MDA5, Melanoma Differentiation-Associated protein 5; MX1, Myxovirus Resistance Protein 1; NAD, nicotinamide adenine dinucleotide; NCR1, Natural cytotoxicity triggering receptor 1; OSM, Oncostatin M; PD-L1, Programmed Death-Ligand 1; PPIA, Peptidylprolyl Isomerase A; PPIB Peptidylprolyl Isomerase B; RSAD2, Radical S-adenosyl methionine domain-containing protein 2; RSV, respiratory syncytial virus; RT-qPCR, quantitative reverse transcription PCR; RV, rhinovirus; sPLA2, secretory Phospholipase A2; TGFb, Transforming Growth Factor beta; THBS1, Thrombospondin 1; TNF, Tumor Necrosis Factor; TLR2, Toll-like Receptor 2.

cell biology↗

bioRxiv2016

Detection of statistically significant network changes in complex biological networks

1 MotivationBiological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localised re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to discovery of novel relevant signatures.\n\n2 ResultsSeveral procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.\n\n3 AvailabilityThe scripts implementing the proposed algorithms are available in R at https://sites.google.com/site/raghvendramallmlresearcher/codes.\n\n4 Contactrmall@qf.org.qa

Bioinformatics↗